Complete Machine Learning course

Complete Machine Learning course, available at $49.99, has an average rating of 4.3, with 20 lectures, based on 38 reviews, and has 96 subscribers.

You will learn about Basics of machine learning Linear Regression Logistic Regression KNN alogrithm Clustering K-Means Clustering Principal component analysis Data preprocsseing EDA The Machine Learning Process Naive Bayes Classifier Supervised learning and unsupervised learning Confusion Matrix The Elbow Method Feature Scaling Feature Scaling Make Predictions Splitting your data into a Training set and a Test set Classification Machine Learning preparation Ordinary Least Squares Accuracy Decision Tree algorithm Random forest algorithm This course is ideal for individuals who are Anyone interested in Data Science or Data Science professionals or Machine learning engineer or Learner who want to use Machine Learning to their CV or career toolkit It is particularly useful for Anyone interested in Data Science or Data Science professionals or Machine learning engineer or Learner who want to use Machine Learning to their CV or career toolkit.

Enroll now: Complete Machine Learning course

Summary

Title: Complete Machine Learning course

Price: $49.99

Average Rating: 4.3

Number of Lectures: 20

Number of Published Lectures: 20

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Basics of machine learning
Linear Regression
Logistic Regression
KNN alogrithm
Clustering
K-Means Clustering
Principal component analysis
Data preprocsseing
EDA
The Machine Learning Process
Naive Bayes Classifier
Supervised learning and unsupervised learning
Confusion Matrix
The Elbow Method
Feature Scaling
Feature Scaling
Make Predictions
Splitting your data into a Training set and a Test set
Classification
Machine Learning preparation
Ordinary Least Squares
Accuracy
Decision Tree algorithm
Random forest algorithm

Who Should Attend

Anyone interested in Data Science
Data Science professionals
Machine learning engineer
Learner who want to use Machine Learning to their CV or career toolkit

Target Audiences

Anyone interested in Data Science
Data Science professionals
Machine learning engineer
Learner who want to use Machine Learning to their CV or career toolkit

This course will cover following topics

1. Basics of machine learning

2. Supervised and unsupervised learning

3. Linear regression

4. Logistic regression

5. KNN Algorithm

6. Naive Bayes Classifier

7. Random forest Algorithm

8. Decision Tree Algorithm

7. Principal component analysis

8. K means clustering

9. Agglomerative clustering

10. There will practical exercise based on Linear regression, Logistic regression ,Naive Bayes, KNN algorithm, Random forest, Decision tree, K Means, PCA .

11. There will be quiz for each topics and total 200 Questions on machine learning course

We will look first in to linear Regression, where we will learn to predict continuous variables and this will details of Simple and Multiple Linear Regression, Ordinary Least Squares, Testing your Model, R Squared and Adjusted R Squared.

We will get full details of Logistic Regression, which is by far the most popular model for Classification. We will learn all about Maximum Likelihood, Feature Scaling, The Confusion Matrix, Accuracy Ratios and you will build your very first Logistic Regression

We will look in to Naive bias classifier which will give full details of Bayes Theorem, implementation of Naive bias in machine learning. This can be used in Spam Filtering, Text analysis, Recommendation Systems.

Random forest algorithm can be used in regression and classification problems. This gives good accuracy even if

data is incomplete.

Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems.

We will look in to KNN algorithm which will working way of KNN algorithm, compute KNN distance matrix, Makowski distance, live examples of implementation of KNN in industry.

We will look in to PCA, K means clustering, Agglomerative clustering which will be part of unsupervised learning.

Along all part of machine supervised and unsupervised learning , we will be following data reading , data prerprocessing, EDA, data scaling, preparation of training and testing data along machine learning model selection , implemention and prediction of models.

Course Curriculum

Chapter 1: Basics of machine learning

Lecture 1: Introduction to Machine learning course

Lecture 2: Basics of machine learning, data in machine learning

Lecture 3: Supervised learning, Unsupervised learning , advantages and disadvantages of ML

Lecture 4: ML life cycle, Exploratory data analysis , ML Challenges and libraries

Chapter 2: Linear Regression

Lecture 1: Linear and multiple linear regression, cost function, gradient decent method

Lecture 2: practical exercise – car price prediction model using linear regression

Lecture 3: Assumptions, Advantages and disadvantage, best practices, MAE, MAPE,MSE L regres

Chapter 3: Logistic regression

Lecture 1: Logistic regression

Lecture 2: pratical exerice – Heart disease analysis using logistic regression

Chapter 4: KNN Algorithm

Lecture 1: KNN Algorithm

Lecture 2: Practical exercise using KNN Algorithm for Tumor classification

Chapter 5: Naïve Bayes Algorithm

Lecture 1: Naïve Bayes Algorithm

Lecture 2: Practical excerise using Navie Bayes for SPAMs

Chapter 6: Random forest algorithm

Lecture 1: Random forest alorgthim

Lecture 2: Practical example using Random forest algorithm

Chapter 7: decision tree algorithm

Lecture 1: decision tree algorithm

Lecture 2: Practical example using decision tree algorithm

Chapter 8: Unsupervised learning

Lecture 1: Unsupervised learning , type of unsupervised learning, adv and disadvantages etc

Chapter 9: PCA and live exercise on unsupervised learning.

Lecture 1: Principal component analysis

Lecture 2: live exercise on unsupervised learning.

Instructors

Satyendra Singh (NCFM and NSIM certified ) Technical and analyst, portfolio manager,Machine learning expert
Stock market Analyst (NSE ACEDEMY NCMP- Level 1 Awarded)

Rating Distribution

1 stars: 5 votes
2 stars: 2 votes
3 stars: 1 votes
4 stars: 2 votes
5 stars: 28 votes

Frequently Asked Questions

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